Name
Abstract | ||
|---|---|---|
Certain problems have characteristics that present difficulties for metaheuristics: their objective function may be either prohibitively expensive, or they may only give a partial ordering over the solutions, lacking a suitable gradient to guide the search. In such cases, it may be more efficient to use a surrogate fitness function to replace or supplement the objective function. This paper provides a broad perspective on surrogate fitness functions, described in the form of a metaheuristic design pattern. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2739482.2768499 | GECCO (Companion) |
Field | DocType | Citations |
Mathematical optimization,Computer science,Fitness function,Fitness approximation,Artificial intelligence,Partially ordered set,Machine learning,Design pattern,Metaheuristic | Conference | 4 |
PageRank | References | Authors |
0.43 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander E.I. Brownlee | 1 | 144 | 18.46 |
| John R. Woodward | 2 | 274 | 17.48 |
| Jerry Swan | 3 | 196 | 19.47 |